Artificial intelligence (AI) now assists clinicians in ways once unthinkable—flagging abnormalities, scoring cytology images, even suggesting treatment plans. Yet a recent Lancet Gastroenterology & Hepatology study revealed a paradox: when doctors who had relied on AI returned to unaided colonoscopy, their adenoma detection rate fell by six percentage points. It was the first quantifiable evidence that continuous AI exposure can quietly dull core clinical vigilance.
Similar regressions have surfaced in radiology, where algorithmic flagging sometimes reduced diagnostic attention; in cognitive science research describing AI-induced skill decay and automation bias; and in legal practice, where attorneys were sanctioned for filing AI-generated cases that never existed. Together, these episodes demonstrate that automation changes human behavior as much as it changes workflow.
“True innovation strengthens the clinician; it never replaces them. Our responsibility is to ensure that every digital advance reinforces, not rewires, professional judgment.” — Mark F. Magazu, II, MPA, JD – Principal, Strategy & Transformation
The Hidden Risk Behind Automation
Veterinary medicine is now at the same crossroads. AI tools promise faster, more accurate diagnostics, but they also invite dependency. In cognitive science, this erosion of independent performance is termed AI-induced skill decay: when professionals begin to trust an external system more than their own pattern recognition. Over time, this shifts clinicians from active interpreters to passive validators—an effect seen in both cockpit automation and diagnostic imaging suites.
Lessons From Other Industries
Fields that adopted automation earlier offer useful models for prevention. Aviation introduced mandatory manual-flight intervals after automation complacency caused safety lapses. In manufacturing, “human-in-the-loop” audits ensure quality control through random manual verification. And in healthcare education, training standards now emphasize deliberate practice without AI assistance to preserve diagnostic intuition (NEJM).
Industry-Level Mitigations
Veterinary organizations can address skill decay through structural safeguards that maintain clinician engagement and accountability:
1. Governance and Standards
Develop formal AI governance frameworks that require validation testing, audit intervals, and human review before algorithmic decisions are finalized. Accrediting bodies should treat skill retention as a measurable quality indicator, just like infection control or anesthesia safety.
2. Training Design
Alternate AI-assisted and unaided casework within continuing education programs. Deliberate alternation preserves manual acuity and builds awareness of how AI affects diagnostic behavior.
3. Feedback and Monitoring Systems
Track discrepancies between AI outputs and clinician conclusions. Continuous feedback identifies performance drift and informs retraining priorities, much like safety data tracking in aviation.
Personal-Level Mitigations
Individual clinicians also play a decisive role in preventing their own skill decay. AI literacy must evolve into a form of professional self-discipline:
1. Maintain Manual Practice
Regularly perform unaided interpretations, especially in imaging and cytology. This sustains the neural patterns associated with expertise—just as pilots log manual flight hours to stay sharp.
2. Challenge the Algorithm
When AI produces a diagnosis or prioritization, clinicians should verify its reasoning against their own mental model. This active comparison guards against automation bias and reinforces critical thinking.
3. Stay Informed on Model Behavior
Understand how the AI was trained, what data it uses, and where it tends to fail. Knowing these boundaries prevents overconfidence in its accuracy.
“In surgery or imaging, instinct remains the final checkpoint. If AI gets us there faster, that’s progress—but the pilot must always keep a hand on the controls.” — Mark F. Magazu, DVM – Principal, Leadership & Governance
Ethical Stewardship and Equity
Technology can narrow or widen care gaps depending on who stays fully engaged. Practices that combine automation with mentorship and skill preservation will achieve both equity and excellence. Education leaders should embed AI competency—not dependency—into curricula.
“Technology should never be a substitute for presence. The clinician’s attention, empathy, and judgment remain the most equitable technologies we have.” — Melissa Magazu-Johnsonbaugh – Principal, Practice & Standards
The Path Forward
Skill decay is not inevitable; it is preventable. By implementing governance at the institutional level and vigilance at the personal level, the veterinary profession can build a culture where AI enhances, rather than replaces, human mastery. The most advanced hospitals of the next decade will not be those that automate fastest—but those that integrate most wisely.
